The present invention relates to the analysis of activities in videos, and more particularly to accurately determining and distinguishing object movements and activities represented thereby.
Video surveillance enables object monitoring through video displays of one or more areas remote from a human monitor. Exemplary applications include security surveillance of public and private areas, for example parking lots for human and vehicle movements, assembly areas such as train stations and entertainment halls for abandoned baggage or objects, borders and doorways for unauthorized entry, secured areas for unauthorized vehicle or object movements and removals, etc. However, human review and analysis of video feeds is time consuming and perhaps inefficient with respect to human resources allocations, and accordingly it is desirable to implement automated systems for video analysis.
Automated analysis of videos for determining object movements, activities and behaviors presents a number of challenges. Variable volumes of activity data, weather conditions, human or object crowding within a scene, geographical area features and other factors often prove problematic for accurate results in making such determinations through video analytics algorithms.